Nox Medical’s director of market access Snorri Helgason recently authored an article, published by SleepLab magazine, evaluating whether the apnea-hypopnea index is truly the optimal metric for measuring the severity of sleep disordered breathing. Below are a few highlights on this topic from Helgason’s article:
Since sleep apnea was defined in the 1970s as a disease, the definition of the disease and how to measure its severity has been categorized through the application of the apnea index, then later the apnea-hypopnea index (AHI).¹
Over the years, sleep medicine has established its diagnostic approach for sleep apnea predominantly using the AHI. This metric holds significant influence as regulatory bodies, payors, and providers rely on it to make crucial patient diagnostic decisions. The AHI plays a pivotal role in categorizing patients according to the severity of their condition and determining whether they receive treatment for obstructive sleep apnea (OSA). However, throughout the years, the question has been raised by many in the field: Is the AHI truly the optimal metric for these purposes?
The correlation between the AHI and clinical outcomes has been found to be inadequate. Since the AHI threshold is based on population averages, it fails to account for the unique characteristics of individual patients, Helgason writes.¹ ² ³ ⁴
“This means that symptomatic patients may exhibit a low AHI, while asymptomatic individuals may be diagnosed with a high AHI. In such cases, healthcare providers face a challenge in determining the appropriate course of action,” he continues. “Furthermore, the usefulness of the AHI as a metric in clinical settings is called into question when it is not strongly linked to symptoms or the risk of developing comorbid chronic diseases, such as cardiovascular disease, and does not provide information on the severity of the single events as they occur, presence of significant oxygen desaturation, ECG abnormalities, and sympathetic activation, that may imply more significant pathology than the AHI alone.” ³ ⁴
Over time, there have been changes in the definition of the AHI, including notable modifications in how oxygen saturation and breathing cessation are defined, as well as the inclusion of arousals with hypopnea events.
The AHI alone cannot help evaluate symptoms like cognitive impairment, daytime sleepiness, or cardiovascular complications. Additionally, the methods employed in deriving the AHI can vary significantly in clinical practice. The measurement and scoring techniques used to calculate the AHI differ among sleep laboratories and devices, resulting in potential inconsistencies and inter-laboratory variability. These variations can compromise the accuracy and reliability of the AHI as a metric, making it challenging to draw direct comparisons between studies and treatment outcomes, Helgason points out.⁵ ⁶
While the sleep field has made significant strides in making sleep diagnostic studies more accessible, relying solely on the AHI and even employing various approaches to calculate the AHI raises valid concerns (e.g., using derived, indirect signals that correlate with AHI but do not measure airflow or effort directly).
In some cases, the ease and accessibility offered by simplified or oversimplified tests may come at the expense of more detailed and thorough diagnostic assessments. Though these tests can provide initial screening or basic insights into sleep disorders, they may not capture the intricacies needed for accurate diagnosis and personalized treatment planning. A broader perspective is needed to ensure that sleep apnea care and diagnosis are approached holistically, considering a range of factors beyond a single metric to achieve better patient outcomes.
“To comprehensively address the patient journey of individuals with sleep apnea and enhance their outcomes, it is imperative to identify more effective tools for personalizing the treatment pathway. It is crucial to reevaluate the approach of condensing an entire night’s sleep into a single numerical value, particularly when this value, such as the AHI, exhibits weak correlations with symptoms and clinical outcomes,” he says.
To read the full article, see the full edition of SleepLab magazine.
- Pevernagie DA, Gnidovec-Strazisar B, Grote L, et al. On the rise and fall of the apnea−hypopnea index: A historical review and critical appraisal. J Sleep Res. 2020;29(4):e13066.
- Malhotra A, Ayappa I, Ayas N, et al. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep. 2021;44(7): zsab030. doi:10.1093/sleep/zsab030
- Soori R, Baikunje N, D’sa I, Bhushan N, Nagabhushana B, Hosmane GB. Pitfalls of AHI system of severity grading in obstructive sleep apnoea. Sleep Sci. 2022;15(Spec 1):285-288. doi:10.5935/1984-0063.20220001 7.
- Lim DC, Mazzotti DR, Sutherland K, et al. Reinventing Polysomnography in the Age of Precision Medicine. Sleep Med Rev. 2020; 52:101313. doi: 10.1016/j.smrv.2020.101313
- Lee YJ, Lee JY, Cho JH, Choi JH. Interrater reliability of sleep stage scoring: a meta-analysis. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine. 2022;18(1):193-202. doi: https://doi.org/10.5664/jcsm.9538
- Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102 doi:10.5664/jcsm.9808
Topic: Industry News